US7095876B2 - Process for the stabilization of the images of a scene correcting offsets in grey levels, detection of mobile objects and harmonization of two snapshot capturing apparatuses based on the stabilization of the images - Google Patents

Process for the stabilization of the images of a scene correcting offsets in grey levels, detection of mobile objects and harmonization of two snapshot capturing apparatuses based on the stabilization of the images Download PDF

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US7095876B2
US7095876B2 US10/208,883 US20888302A US7095876B2 US 7095876 B2 US7095876 B2 US 7095876B2 US 20888302 A US20888302 A US 20888302A US 7095876 B2 US7095876 B2 US 7095876B2
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images
offsets
stabilization
scene
image
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US20030031382A1 (en
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Michel Broekaert
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Safran Electronics and Defense SAS
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Sagem SA
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/277—Analysis of motion involving stochastic approaches, e.g. using Kalman filters

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  • the invention relates to the electronic stabilization of the images captured by an observation apparatus of an imaging system, such as portable thermal observation binoculars or observation or guidance cameras.
  • the carrier of the apparatus may be in motion, on the one hand, and create all kinds of vibrations, on the other hand, and, from a certain magnification onwards, one no longer sees anything on the images which are too blurred. They must then be stabilized in order to circumvent the effects related to the trajectory of the carrier and also those caused by the vibrations to which the apparatus is subjected, in short, to compensate for the 3D motions of the apparatus.
  • stabilization will be regarded as involving the mutual registration of the successive images supplied by the observation apparatus. More precisely, image k+1 differing from image k owing to rotations in roll, pitch and yaw, change of focal length (in the case of a camera whose zoom factor may be varied), translations and angular and linear vibrations, it is necessary to impose opposite zoom factors and rotations in order to stabilize image k+1 with respect to image k.
  • the invention relates to a process for the electronic stabilization of the images of a scene of a snapshot capturing apparatus of an imaging system in which, in a terrestrial reference frame, the images of the scene captured by the apparatus are filtered in a low-pass filter, so as to retain only the low spatial frequencies thereof, and the optical flow equation is solved to determine the rotations to be imposed on the images in order to stabilize them with regard to the previous images.
  • the reference frame of the process of the invention is no longer an inertial reference frame but a terrestrial reference frame.
  • the low-pass filtering is based on the following assumption. Few objects are moving with respect to the scene and it is therefore possible to make do with a process for motion compensation by predicting the motion of the apparatus, establishing a linear model describing the parameters with the motion of the apparatus (zoom, yaw, pitch, roll and focal length) and estimating these parameters with the aid of the optical flow equation, for the low, or even very low, frequencies of the images which correspond to the scene. At the low frequencies, only the big objects of the scene are retained, the small objects and the transitions of the contours being erased.
  • the optical flow equation measures the totality of the displacements of the apparatus.
  • the carrier and the snapshot capturing apparatus have the same trajectory but that the apparatus additionally undergoes angular and linear vibrations which may be considered to be zero-mean noise, white or otherwise depending on the spectrum of the relevant carrier.
  • the linear vibrations will be neglected on account of the observation distance and of their small amplitude with respect to the displacements of the carrier.
  • optical flow equation is solved by the method of least squares.
  • the displacements due to the trajectory of the apparatus are determined by estimation, for example by averaging, or filtering in a Kalman filter, the state vector of the snapshot capturing apparatus.
  • the invention also relates to a process for correcting offsets in grey levels of a snapshot capturing apparatus of an imaging system whose images are stabilized according to the process of the invention, characterized in that the calculation of the offsets is deduced from the steps of the stabilization process.
  • FIG. 1 illustrates the geometry of the motion of a snapshot capturing camera
  • FIG. 2 is a functional diagram of the imaging system allowing the implementation of the process for the electronic stabilization of images of the invention
  • FIG. 3 is an illustration of the propagation of the offset corrections
  • FIG. 4 is the flowchart of the stabilization process incorporating the implementation of the offsets correction algorithm.
  • This may be a video camera or an infrared camera.
  • the points of the scene which are viewed by the camera between two images are linked by the trajectory of the carrier.
  • the camera is in a three-dimensional Cartesian or polar coordinate system with the origin placed at the front lens of the camera and the z axis directed along the direction of aim.
  • the position of the camera with respect to the centre of gravity of the carrier is defined by three rotations (ab, vc, gc) and three translations (Txc, Tyc, Tzc).
  • the relationship between the 3D coordinates of the camera and those of the carrier is:
  • x ⁇ ( t ) F ⁇ ( t ) ⁇ x ⁇ ( t ) + u ⁇ ( t ) + v ⁇ ( t )
  • H(t) is an m ⁇ n matrix dependent on t and w is Gaussian white noise of dimension m, which may be considered to be the angular and linear vibrations of the camera with respect to the centre of gravity of the carrier.
  • the discrete model may be written:
  • angles and translations to which the camera is subjected with respect to the centre of gravity are not constant in the course of the trajectory, in a viewfinder for example, it is sufficient to describe their measured or commanded values (ac(t), bc(t), gc(t), Txc(t), Tyc(t), Tzc(t) as a function of t or of k.
  • the trajectory of the camera can be defined by a vector xc k+1
  • x ⁇ ⁇ c k + 1 R ⁇ ( a ⁇ ⁇ c , b ⁇ ⁇ c , g ⁇ ⁇ c ) * ( F k * x k + u k + v k ) + T ⁇ ⁇ c
  • the camera undergoes pure 3D rotations and three translations, whose values are supplied by the vector x′ k+1 .
  • FIG. 1 shows the geometry of the motion of the camera in the 3D space of the real world.
  • the camera is in a three-dimensional Cartesian or polar coordinate system with the origin placed at the front lens of the camera and the z axis directed along the direction of aim.
  • ( x′,y′,z ′)′ R ( da,db,dg )*( x,y,z )′+ T ( Tx, Ty, Tz ) where
  • linear and angular vibrations may be considered to be zero -mean noise, white or otherwise depending on the spectrum of the relevant carrier.
  • optical flow equation may be written:
  • the low-pass filtering consists, conventionally, in sliding a convolution kernel from pixel to pixel of the digitized images from the camera, in which kernel the origin of the kernel is replaced by the average, of the grey levels of the pixels of the kernel.
  • the results obtained with a rectangular kernel 7 pixels high (v) and 20 pixels wide (H) are very satisfactory in normally contrasted scenes.
  • v pixels high
  • H pixels wide
  • Wavelet functions can also be used as averaging kernel.
  • a pyramid-shaped averaging kernel (triangle along X convolved with triangle along Y) has therefore been used.
  • Wavelet functions may also be used as averaging kernel.
  • the optical flow equation measures the totality of the displacements of the camera. It was seen earlier that the camera motions deduced from those of the carrier and the actual motions of the camera could be more finely distinguished by saying that the carrier and the camera have the same trajectory, but that the camera additionally undergoes linear and angular vibrations.
  • the displacements due to the trajectory of the camera are contained in the state vector x′ k+1 of the camera, or rather in the estimation which can be made thereof, by averaging, or by having a Kalman filter which supplies the best estimate thereof.
  • This may be relevant to applications where one wishes to accumulate frames free of trail, or if one wishes to keep an absolute reference of the landscape (the dynamic harmonization of a homing head and of a viewfinder for example).
  • this may also relate to applications where one will seek to restore the landscape information in an optimal manner by obtaining an image ridded of the effects of sampling and of detector size.
  • image k + 1 ⁇ ( X , Y ) image ⁇ ( X - dX k + 1 ⁇ ( X , Y ) , Y - dY k + 1 ⁇ ( X , Y ) )
  • the snapshot capturing camera 1 delivers its video signal of images to a low-pass filter 2 as well as to a processing block 3 receiving the stabilization data on a second input and supplying the stabilized images as output. On its second input, the block 3 therefore receives the rates of rotation to be imposed on the images captured by the camera 1 .
  • the output of the filter 2 is linked to two buffer memories 4 , 5 respectively storing the two filtered images of the present instant t and of the past instant t ⁇ 1.
  • the two buffer memories 4 , 5 are linked to two inputs of a calculation component 6 , which is either an ASIC or an FPGA (field programmable gate array).
  • the calculation component 6 is linked to a work memory 7 and, at output, to the processing block 3 . All the electronic components of the system are controlled by a management microcontroller 8 .
  • the invention is of interest since it makes it possible to correct the offsets of the grey levels, that is to say the shifts existing between the grey levels of the various pixels of the matrix of the detectors of the camera and that of a reference pixel, without having to place the camera in front of a uniform background, such as a black body.
  • the image stabilization provides a means which is equivalent to the offset correction microscan.
  • the image stabilization proposed in the present patent application therefore simulates the microscan required for grey levels offset correction and the optical flow equation allows the calculation of the offsets which is therefore deduced from the steps of the stabilization process.
  • I ⁇ stab n ⁇ 1 be image I n ⁇ 1 stabilized on image I n .
  • the defect ⁇ a of the image I stab n ⁇ 1 is the same as that of the image In, but shifted in space, since the scene is registered with respect to the scene viewed by I n .
  • this second algorithm is, on the basis of two frames, to propagate the offset of the pixels along what may be called chains.
  • the principle of the first algorithm was to take the difference between frame I n and frame I n ⁇ 1 stabilized with respect to frame I n . Stated otherwise, the offset at the point (x, y) was rendered equal to the offset at the point (x ⁇ mx(x, y),y ⁇ my(x,y)), if (mx(x,y),my(x,y)) is the displacement making it possible to find the pixel antecedent to (x,y) through the motion. This is in fact the motion of the point (x ⁇ mx(x,y),y ⁇ mx(x,y)).
  • Offsets n (x1,y1) I n ( x 1 ,y 1) ⁇ I n ⁇ 1 ( x 1 ⁇ mx ( x 1 ,y 1), y ⁇ my ( x 1 ,y 1))
  • I n ⁇ 1 ( x 1 ,y 1) I n ⁇ 1 ( x 1 ,y 1) ⁇ Offset( x 1 ,y 1) (48)
  • the offset common to all the pixels of the chain is the offset of its first pixel (x0,y0).
  • the offset differential we shall see that the offsets of the neighbouring chains will get mixed up.
  • FIG. 3 The principle of the propagation of the offset corrections is illustrated by FIG. 3 .
  • Pixel 0 is the first pixel of the chain and has no counterpart in image I n ⁇ 1 .
  • To correct pixel i we firstly calculate the difference between the uncorrected pixel i of image I n and pixel i ⁇ 1, corrected at iteration i ⁇ 1, of image_I n ⁇ 1 , then we undertake the correction of pixel i in image I n ⁇ 1 .
  • the propagation error will be partly corrected by a temporal filtering of the offsets frames of temporal averaging type and a temporal management of the length of the chains of pixels.
  • Off_filt n 1 ⁇ + 1 ⁇ ⁇ ( ⁇ - 1 ) ⁇ Off_filt n - 1 + Off a + Off a - 1 ⁇
  • the first filter is adapted more to an evaluation of the offset, the second, to a maintaining of the offset, with a time constant t of the order of magnitude of the duration after which the offset is regarded as having varied.
  • a filtering of the infinite type makes it possible to ally a relatively fast convergence to the offset regarded as fixed on a scale of several minutes, with good elimination of noise.
  • the filter converges fairly rapidly at the start, but, on account of its very large phase delay will converge more and more slowly, and thereby even acquire a certain bias.
  • a certain number of feedbacks are performed, together with management of the length of the chains.
  • step 43 of the optical flow equation, supplies the angles of roll ⁇ , of pitch ⁇ and of yaw ⁇ of the camera as a function also of which, in step 44 , are determined the translations ⁇ x, ⁇ y to be applied so as, in step 45 , to stabilize the image I n ⁇ 1 , with respect to image I n and, in step 46 , to take the difference between the two.
  • the offsets are obtained.
  • the objective is rather to obtain a correction of the offset over the entire spectrum of spatial frequencies.
  • a filter with infinite mean is used, adopted in respect of evaluation, and, knowing that there is a bias in the measurement owing to the phase delay of the filter, we operate a feedback of the offset after convergence, a certain number of times. The feedback is carried out when the sum of the differences between two arrays of consecutive filtered offsets goes below a certain threshold.
  • the offsets are determined by an algorithm ( 47 ) for calculating image differences ( 46 ), preferably, by an offset corrections propagation algorithm ( 47 ).
  • a temporal filtering of the offsets frames is carried out ( 48 ), followed by a convergence test ( 49 ) and by a management ( 50 ) of the length of the chains of pixels so as to eliminate the propagation errors.
  • feedbacks of the offsets are carried out ( 51 ).
  • each image of the imaging system may be said to be harmonized with the previous image.
  • the two images of the two devices are captured and they are stabilized with respect to one another, that is to say the two devices are harmonized.
  • Harmonizing amounts to merging the optical axes of the two devices and to matching the pixels of the two images pairwise and, preferably, also to merging these pixels.
  • the two devices to be harmonized according to this process must be of the same optical nature, that is to say operate in comparable wavelengths.
  • the invention also relates to a process for the electronic harmonization of two snapshot capturing apparatuses of two imaging systems both capturing images of the same scene, in which, in a terrestrial reference frame, the images of the scene captured at the same instants by the two apparatuses are filtered in a low-pass filter, so as to retain only the low spatial frequencies thereof, and the optical flow equation between these pairs of respective images of the two apparatuses is solved so as to determine the rotations and the variation of the relationship of the respective zoom parameters to be imposed on these images so as to harmonize them with one another.

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US10/208,883 2001-07-31 2002-07-31 Process for the stabilization of the images of a scene correcting offsets in grey levels, detection of mobile objects and harmonization of two snapshot capturing apparatuses based on the stabilization of the images Expired - Lifetime US7095876B2 (en)

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Application Number Priority Date Filing Date Title
FR0110243A FR2828314B1 (fr) 2001-07-31 2001-07-31 Procede de stabilisation electronique des images d'une scene d'un appareil de prise de vues d'un systeme d'imagerie et procede d'harmonisation electronique de deux appareils de prise de vues de deux systemes d'imagerie
FR0110243 2001-07-31
FR0202081 2002-02-19
FR0202081A FR2828315B1 (fr) 2001-07-31 2002-02-19 Stabilisation des images d'une scene, correction des offsets de niveaux de gris, detection d'objets mobiles et harmonisation de deux appareils de prise de vues fondes sur la stabilisation des images

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US20050094852A1 (en) * 2003-09-05 2005-05-05 The Regents Of The University Of California Global motion estimation image coding and processing
US20050167498A1 (en) * 2004-01-08 2005-08-04 Kunihiko Ito Method and apparatus for optically picking up an image of an information code
US20090087119A1 (en) * 2007-09-28 2009-04-02 Canon Kabushiki Kaisha Method and apparatus for arbitrary ratio image reduction
US20090309984A1 (en) * 2006-06-29 2009-12-17 Thales Hybrid image stabilization for video camera
US20110199489A1 (en) * 2010-02-18 2011-08-18 Testo Ag Method for the extraction of an ir-image and thermal imaging camera
US8872911B1 (en) * 2010-01-05 2014-10-28 Cognex Corporation Line scan calibration method and apparatus

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US7596284B2 (en) * 2003-07-16 2009-09-29 Hewlett-Packard Development Company, L.P. High resolution image reconstruction
FR2863750B1 (fr) * 2003-12-15 2006-03-03 Sagem Procede et dispositif de stabilisation d'images acquises en lignes ou en colonnes
US7433497B2 (en) * 2004-01-23 2008-10-07 Hewlett-Packard Development Company, L.P. Stabilizing a sequence of image frames
US8036494B2 (en) * 2004-04-15 2011-10-11 Hewlett-Packard Development Company, L.P. Enhancing image resolution
US7730406B2 (en) * 2004-10-20 2010-06-01 Hewlett-Packard Development Company, L.P. Image processing system and method
US7583819B2 (en) * 2004-11-05 2009-09-01 Kyprianos Papademetriou Digital signal processing methods, systems and computer program products that identify threshold positions and values
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FR2909473B1 (fr) 2006-12-01 2009-05-15 Sagem Defense Securite Correction de bruit dans des images captees par un systeme a stabilisation inertielle
FR2927448B1 (fr) 2008-02-12 2011-03-04 Sagem Defense Securite Correction d'images captees et stabilisees
EP2415015B1 (fr) * 2009-03-30 2015-08-12 Telefonaktiebolaget LM Ericsson (publ) Traitement de code à barres
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US9159133B2 (en) * 2012-11-05 2015-10-13 Qualcomm Incorporated Adaptive scale and/or gravity estimation
CN104010130B (zh) * 2014-05-05 2017-02-15 中国科学院国家天文台 一种多窗口跟踪的稳像方法
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CN110796010B (zh) * 2019-09-29 2023-06-06 湖北工业大学 一种结合光流法和卡尔曼滤波的视频稳像方法
CN111968066B (zh) * 2020-08-27 2023-01-10 烟台艾睿光电科技有限公司 红外图像的校正方法、装置、设备及制冷红外成像系统

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US7349583B2 (en) * 2003-09-05 2008-03-25 The Regents Of The University Of California Global motion estimation image coding and processing
US20050167498A1 (en) * 2004-01-08 2005-08-04 Kunihiko Ito Method and apparatus for optically picking up an image of an information code
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US8111294B2 (en) * 2006-06-29 2012-02-07 Thales Hybrid image stabilization method and apparatus
US20090087119A1 (en) * 2007-09-28 2009-04-02 Canon Kabushiki Kaisha Method and apparatus for arbitrary ratio image reduction
US8872911B1 (en) * 2010-01-05 2014-10-28 Cognex Corporation Line scan calibration method and apparatus
US20110199489A1 (en) * 2010-02-18 2011-08-18 Testo Ag Method for the extraction of an ir-image and thermal imaging camera

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EP1298592B1 (fr) 2014-04-16
EP1298592A1 (fr) 2003-04-02
FR2828315A1 (fr) 2003-02-07
US20030031382A1 (en) 2003-02-13
FR2828315B1 (fr) 2005-08-05

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